library(tidyverse)
## Warning: package 'tidyverse' was built under R version 3.2.5
## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Warning: package 'ggplot2' was built under R version 3.2.4
## Warning: package 'tibble' was built under R version 3.2.5
## Warning: package 'tidyr' was built under R version 3.2.5
## Warning: package 'readr' was built under R version 3.2.5
## Warning: package 'purrr' was built under R version 3.2.5
## Warning: package 'dplyr' was built under R version 3.2.5
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag(): dplyr, stats
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy))
## #### ##
## EGGS ##
## #### ##
ggplot(data = mpg)
nrow(mtcars)
## [1] 32
ncol(mtcars)
## [1] 11
?mpg
ggplot(data = mpg) +
geom_point(mapping = aes(x = cyl, y = hwy))
ggplot(data = mpg) +
geom_point(mapping = aes(x = drv, y = class))
## ## ## ## ## ## ## ##
## Aesthetic Mapping ##
## ## ## ## ## ## ## ##
# Check if outlier cars with higher than avg mileage and rel. large displacements
# Are subcompact vehicles
# Use different levels of an aesthetic property (diff levels of size, cols, shape...2)
ggplot(mpg) +
geom_point(aes(x = displ, y = hwy, col = class))
## #### ##
## EGGS ##
## #### ##
ggplot(data = mpg) +
geom_point(
mapping = aes(x = displ, y = hwy, color = "blue")
) # Not blue because color inside of aes? it's as if color = x, with x <- "blue"
# Instead, do:
ggplot(data = mpg) +
geom_point(
mapping = aes(x = displ, y = hwy), color = "blue"
)
# Mapping a continous var to color, size, shape. Then categorical
str(mpg)
## Classes 'tbl_df', 'tbl' and 'data.frame': 234 obs. of 11 variables:
## $ manufacturer: chr "audi" "audi" "audi" "audi" ...
## $ model : chr "a4" "a4" "a4" "a4" ...
## $ displ : num 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
## $ year : int 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
## $ cyl : int 4 4 4 4 6 6 6 4 4 4 ...
## $ trans : chr "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
## $ drv : chr "f" "f" "f" "f" ...
## $ cty : int 18 21 20 21 16 18 18 18 16 20 ...
## $ hwy : int 29 29 31 30 26 26 27 26 25 28 ...
## $ fl : chr "p" "p" "p" "p" ...
## $ class : chr "compact" "compact" "compact" "compact" ...
ggplot(mpg) +
geom_point(aes(x = displ, y = hwy, col = class))
# For continuous variables:
# Color -> gradient
# Size -> selects a couple of sizes
# Shape -> Error: A continuous variable cannot be mapped to shape.
# For categorical vars, works as expected.
# Same variable to multiple aestethics:
ggplot(mpg) +
geom_point(aes(x = displ, y = hwy, col = class, size = class))
## Warning: Using size for a discrete variable is not advised.
# Works as expected, legend is changed accordingly too.
# Stroke aes: for shapes that have a border, can color outside seperatly
ggplot(mpg) + geom_point(aes(x = displ, y = hwy), shape = 21, stroke = 5)
# mapping an aes to something else than a var
ggplot(mpg) +
geom_point(aes(x = displ, y = hwy, col = displ < 5)) # Like magic
## ## ## ###
## FACETS ##
## ## ## ###
# Split cat. data with facets, with each subplot displaying one subset of the data
# First argumnent is R formula object, should be categorical var
ggplot(mpg) +
geom_point(aes(x = displ, y = hwy)) +
facet_wrap(~ class, nrow = 2)
# To facet with a combo of 2 vars, use facet_grid
ggplot(mpg) +
geom_point(aes(x = displ, y = hwy)) +
facet_grid(drv ~ cyl)
# ggplot(mpg) +
# geom_point(aes(x = displ, y = hwy)) +
# facet_grid(cyl~drv)
ggplot(mpg) +
geom_point(aes(x = displ, y = hwy)) +
facet_grid(cyl ~ .) # To not use rows or col.
## #### ##
## EGGS ##
## #### ##
# Try facet-ing a cont variable
ggplot(mpg) +
geom_point(aes(x = displ, y = hwy)) +
facet_wrap(~hwy) # LOTSA values - seems like it takes it like factor
ggplot(mpg) +
geom_point(mapping = aes(x = drv, y = cyl))
# Compare this to facet_grid(drv ~ cyl) empty cells
# Empty cell = empty point in plot above
# For cells that do have the corresponding drv, cyl combo ->
# get a more in depth view on displ and hwy values
## What does this do?
ggplot(mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(drv ~ .) # Plot on one col only
ggplot(mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(. ~ cyl) # plot on one row only
## How about
ggplot(mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(~ cyl) # Same as the above
## Advantages and cons of faceting - compare with 1st facet code
ggplot(mpg) +
geom_point(aes(x = displ, y = hwy, col = class))
# Pro: easier seperation of data (if have a lot of data points)
# Can see finer details for every group
# Con: (and advantage of using aesthetics): how do different variables 'interact'
## ## ## ## ## ##
## Geom Object ##
## ## ## ## ## ##
ggplot(mpg) +
geom_point(aes(x = displ, y = hwy, col = drv)) +
geom_smooth(aes(displ, hwy, col = drv, linetype = drv))
# OR
ggplot(mpg, aes(x = displ, y = hwy)) +
geom_point(aes(color = class)) +
geom_smooth() # Neat
# Can use same idea to specify different
# data for each layer
ggplot(mpg, aes(x = displ, y = hwy)) +
geom_point(aes(color = class)) +
geom_smooth(
data = filter(mpg, class == "subcompact"),
se = F
)
## #### ##
## EGGS ##
## #### ##
ggplot(mpg, aes(x = displ, y= hwy, color = drv)) +
geom_point() +
geom_smooth(se = F)
ggplot(mpg, aes(x = displ, y = hwy)) +
geom_point() +
geom_smooth(se = F)
ggplot(mpg, aes(x = displ, y = hwy)) +
geom_point() +
geom_smooth(se = F, aes(group = drv))
ggplot(mpg, aes(x = displ, y = hwy, col = drv)) +
geom_point() +
geom_smooth(se = F)
ggplot(mpg, aes(x = displ, y = hwy)) +
geom_point(aes(color = drv)) +
geom_smooth(se = F)
ggplot(mpg, aes(x = displ, y = hwy)) +
geom_point(aes(col = drv)) +
geom_smooth(se = F, aes(linetype = drv))
ggplot(mpg, aes(x = displ, y = hwy)) +
geom_point(aes(col = drv))
## ## ## ## ## ## ## ## ##
## Stat. Transformation ##
## ## ## ## ## ## ## ## ##
print("
@note: The algorithm used to calc. new values for a graph
called stat (for statistical transformation)
Each geom uses a default stat argument
@note: ?geom_bar (or other) and search for computed variables
")
## [1] "\n@note: The algorithm used to calc. new values for a graph \ncalled stat (for statistical transformation)\nEach geom uses a default stat argument \n@note: ?geom_bar (or other) and search for computed variables\n "
# Can use geom and stat interchangeably
# e.g:
ggplot(diamonds) + geom_bar(aes(x = cut))
ggplot(diamonds) + stat_count(aes(x = cut))
print("
@note: The above works because every stat
has a default geom and every geom has a def stat.
If want to set your own y for bar chart, use stat = 'identity'
")
## [1] "\n @note: The above works because every stat\nhas a default geom and every geom has a def stat. \n If want to set your own y for bar chart, use stat = 'identity'\n "
# Proportion bar chart
ggplot(diamonds) + geom_bar(aes(x = cut, y = ..prop.., group = 1)) # @question: What does group do?
# Summarize y values for every x
ggplot(diamonds) + stat_summary(aes(x = cut, y = depth),
fun.ymin = min,
fun.ymax = max,
fun.y = median)
?stat_summary
?stat_bin
## #### ##
## EGGS ##
## #### ##
# Defaylt geom associated with stat_summary is geom_pointrange
ggplot(diamonds, aes(x = cut, y = depth)) +
geom_pointrange(stat = "summary", fun.ymin = min, fun.ymax = max, fun.y = median)
?geom_line
ggplot(data = diamonds) +
geom_bar(aes(x = cut, y = ..prop.., fill = cut))
# If group is not set to 1, then all the bars have prop == 1.
# The function geom_bar assumes that the groups are equal to the x values,
# since the stat computes the counts within the group.
## ## ## ## ##
## Pos. Adj ##
## ## ## ## ##
# Can use a diff variable for fill in geom_bar,
# Will atomatically split bars
ggplot(diamonds) +
geom_bar(aes(x = cut, fill = clarity))
# @note: Stacking done automatiocally by position adj. (spec,. by position arg)
# Pos = identity: overlaps bars, but useful for points
ggplot(diamonds, aes(x = cut, fill = clarity)) +
geom_bar(alpha = 1/5, position = "identity")
ggplot(diamonds, aes(x = cut, color = clarity)) +
geom_bar(fill= NA, position = "identity")
# Position = fill works like stacking
# But stacked bars the same height -> better to compare groups
ggplot(diamonds, aes(x = cut, fill = clarity)) +
geom_bar(position = "fill")
# Position = dodge places overlapping objects beside each other
ggplot(diamonds) +
geom_bar(aes(x = cut, fill = clarity), position = 'dodge')
# For scatterplots: to avoid rounding and overlapping of vars
# (How to know if one point is one or 1000 points rounded)
# use pos = jitter to add random noise to data
# reduces chance of overlap because no two points will receive
# same random noise
ggplot(mpg) +
geom_point(aes(x = displ, y = hwy), position = "jitter")
ggplot(mpg) +
geom_point(aes(x = displ, y = hwy))
# Can also do geom_jitter instead of geom_point
# To look up position, do ?position_x (x = dodge, identity, jitter, fill etc.. )
## ## ## ##
## EGGS ##
## ## ## ##
# Add some jitter to the plot
ggplot(mpg) +
geom_point(aes(x = cty, y = hwy), position = "jitter") # Have to put position in geom object
# How to control jittering
ggplot(mpg) +
geom_jitter(aes(x = cty, y = hwy), width = 10, height = 20)
# geom_count
ggplot(mpg) +
geom_count(aes(x = cty, y = hwy))
# geom_count maps count of numbers of observation at each location to the point size
# defualt pos adjustment for geom boxplot -> dodge
?geom_boxplot
ggplot(mpg) +
geom_boxplot(aes(x = cyl, y = hwy, color = class)) #because dodge -> placed side by side
## Warning: position_dodge requires non-overlapping x intervals
# If use pos = identity instead:
ggplot(mpg) +
geom_boxplot(aes(x = cyl, y = hwy, color = class), position = "identity") #overlapped.
## ## ## ## ## ##
## COORD. SYS. ##
## ## ## ## ## ##
# Flip it
ggplot(mpg) +
geom_boxplot(aes(x = class, y = hwy)) +
coord_flip()
## Polar coord
bar <- ggplot(data = diamonds) +
geom_bar(aes(x = cut, fill = cut),
show.legend = F, width = 1) +
theme(aspect.ratio = 1) + labs(x = NULL, y = NULL)
bar + coord_flip()
bar + coord_polar()
## #### ##
## EGGS ##
## #### ##
# Turn bar chart into pie chart
ggplot(diamonds) + # geom_bar, 1 bar with the different cut counts
geom_bar(aes(x = factor(1), fill = cut), width = 1) +
coord_polar(theta = "y") # map angle to y
# ?labs
?labs()
?coord_quickmap()